Processing point clouds using dynamic voxelization

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing point cloud data using dynamic voxelization. When deployed within an on-board system of a vehicle, processing the point cloud data using dynamic voxelization can be used to make autonomous driving decisions for the vehicle with enhanced accuracy, for example by combining representations of point cloud data characterizing a scene from multiple views of the scene.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application is a continuation (and claims the benefit ofpriority under 35 USC 120) of U.S. patent application Ser. No.16/924,080, filed Jul. 8, 2020, which is claims priority to U.S.Provisional Application No. 62/871,676, filed on Jul. 8, 2019. Thedisclosure of the prior applications are considered part of and isincorporated by reference in the disclosure of this application.

BACKGROUND

This specification relates to processing point cloud data using neuralnetworks.

Neural networks are machine learning models that employ one or morelayers of nonlinear units to predict an output for a received input.Some neural networks include one or more hidden layers in addition to anoutput layer. The output of each hidden layer is used as input to ormore other layers in the network, i.e., one or more other hidden layers,the output layer, or both. Each layer of the network generates an outputfrom a received input in accordance with current values of a respectiveset of parameters.

SUMMARY

This specification describes a system implemented as computer programson one or more computers in one or more locations that processes, usinga neural network, point cloud data representing a sensor measurement ofa scene captured by one or more sensors to generate a network outputthat characterizes the scene, e.g., an object detection output thatidentifies locations of one or more objects in the scene or a differentkind of output that characterizes different properties of objects in thescene. For example, the one or more sensors can be sensors of anautonomous vehicle, e.g., a land, air, or sea vehicle, and the scene canbe a scene that is in the vicinity of the autonomous vehicle. Thenetwork output can then be used to make autonomous driving decisions forthe vehicle, to display information to operators or passengers of thevehicle, or both.

The subject matter described in this specification can be implemented inparticular embodiments so as to realize one or more of the followingadvantages. In contrast to existing approaches for processing pointcloud data using neural networks, e.g., for object detection, which usehard voxelization, the described techniques use dynamic voxelization togenerate a representation of a point cloud that will be processed usinga neural network. By making use of dynamic voxelization, the generatedrepresentation preserves the complete raw point cloud, yieldsdeterministic voxel features and serves as a natural foundation forfusing information across different views. This allows for the neuralnetwork that processes the representation to generate task outputs,e.g., object detection or object classification outputs, that are moreaccurate and have higher precision that conventional approaches.Additionally, this specification describes a multi-view fusionarchitecture that can encode point features with more discriminativecontext information extracted from the different views, e.g., abirds-eye view and a perspective view, resulting in more accuratepredictions being generated by the task neural network.

The details of one or more embodiments of the subject matter of thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of an example on-board system.

FIG. 2 is a flow diagram of an example process for processing pointcloud data.

FIG. 3 is an example of generating a dynamic voxel representation for aview of a scene.

FIG. 4 shows an example process for applying multi-view fusion togenerate a network input.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

This specification describes a system implemented as computer programson one or more computers in one or more locations that processes pointcloud data representing a sensor measurement of a scene captured by oneor more sensors to generate an object detection output that identifieslocations of one or more objects in the scene. For example, the one ormore sensors can be sensors of an autonomous vehicle, e.g., a land, air,or sea vehicle, and the scene can be a scene that is in the vicinity ofthe autonomous vehicle. The object detection output can then be used tomake autonomous driving decisions for the vehicle, to displayinformation to operators or passengers of the vehicle, or both.

FIG. 1 is a block diagram of an example on-board system 100. Theon-board system 100 is physically located on-board a vehicle 102. Thevehicle 102 in FIG. 1 is illustrated as an automobile, but the on-boardsystem 100 can be located on-board any appropriate vehicle type. Thevehicle 102 can be a fully autonomous vehicle that makesfully-autonomous driving decisions or a semi-autonomous vehicle thataids a human operator. For example, the vehicle 102 can autonomouslyapply the brakes if a full-vehicle prediction indicates that a humandriver is about to collide with a detected object, e.g., a pedestrian, acyclist, another vehicle. While the vehicle 102 is illustrated in FIG. 1as being an automobile, the vehicle 102 can be any appropriate vehiclethat uses sensor data to make fully-autonomous or semi-autonomousoperation decisions. For example, the vehicle 102 can be a watercraft oran aircraft. Moreover, the on-board system 100 can include componentsadditional to those depicted in FIG. 1 (e.g., a control subsystem or auser interface subsystem).

The on-board system 100 includes a sensor subsystem 120 which enablesthe on-board system 100 to “see” the environment in a vicinity of thevehicle 102. The sensor subsystem 120 includes one or more sensors, someof which are configured to receive reflections of electromagneticradiation from the environment in the vicinity of the vehicle 102. Forexample, the sensor subsystem 120 can include one or more laser sensors(e.g., LIDAR sensors) that are configured to detect reflections of laserlight. As another example, the sensor subsystem 120 can include one ormore radar sensors that are configured to detect reflections of radiowaves. As another example, the sensor subsystem 120 can include one ormore camera sensors that are configured to detect reflections of visiblelight.

The sensor subsystem 120 repeatedly (i.e., at each of multiple timepoints) uses raw sensor measurements, data derived from raw sensormeasurements, or both to generate sensor data 122. The raw sensormeasurements indicate the directions, intensities, and distancestravelled by reflected radiation. For example, a sensor in the sensorsubsystem 120 can transmit one or more pulses of electromagneticradiation in a particular direction and can measure the intensity of anyreflections as well as the time that the reflection was received. Adistance can be computed by determining the time which elapses betweentransmitting a pulse and receiving its reflection. Each sensor cancontinually sweep a particular space in angle, azimuth, or both.Sweeping in azimuth, for example, can allow a sensor to detect multipleobjects along the same line of sight.

In particular, the sensor data 122 includes point cloud data thatcharacterizes the latest state of an environment (i.e., an environmentat the current time point) in the vicinity of the vehicle 102. A pointcloud is a collection of data points defined by a given coordinatesystem. For example, in a three-dimensional coordinate system, a pointcloud can define the shape of some real or synthetic physical system,where each point in the point cloud is defined by three valuesrepresenting respective coordinates in the coordinate system, e.g., (x,y, z) coordinates. As another example, in a three-dimensional coordinatesystem, each point in the point cloud can be defined by more than threevalues, wherein three values represent coordinates in the coordinatesystem and the additional values each represent a property of the pointof the point cloud, e.g., an intensity of the point in the point cloud.Point cloud data can be generated, for example, by using LIDAR sensorsor depth camera sensors that are on-board the vehicle 102. For example,each point in the point cloud can correspond to a reflection of laserlight or other radiation transmitted in a particular direction by asensor on-board the vehicle 102.

The on-board system 100 can provide the sensor data 122 generated by thesensor subsystem 120 to a perception subsystem 130 for use in generatingperception outputs 132.

The perception subsystem 130 implements components that perform aperception task, e.g., that identify objects within a vicinity of thevehicle or classify already identified objects or both. The componentstypically include one or more fully-learned machine learning models. Amachine learning model is said to be “fully-learned” if the model hasbeen trained to compute a desired prediction when performing aperception task. In other words, a fully-learned model generates aperception output based solely on being trained on training data ratherthan on human-programmed decisions. For example, the perception output132 may be a classification output that includes a respective objectscore corresponding to each of one or more object categories, eachobject score representing a likelihood that the input sensor datacharacterizes an object belonging to the corresponding object category.As another example, the perception output 132 can be an object detectionoutput that includes data defining one or more bounding boxes in thesensor data 122, and optionally, for each of the one or more boundingboxes, a respective confidence score that represents a likelihood thatan object belonging to an object category from a set of one or moreobject categories is present in the region of the environment shown inthe bounding box. Examples of object categories include pedestrians,cyclists, or other vehicles near the vicinity of the vehicle 102 as ittravels on a road.

The on-board system 100 can provide the perception outputs 132 to aplanning subsystem 140. When the planning subsystem 140 receives theperception outputs 132, the planning subsystem 140 can use theperception outputs 132 to generate planning decisions which plan thefuture trajectory of the vehicle 102. The planning decisions generatedby the planning subsystem 140 can include, for example: yielding (e.g.,to pedestrians identified in the perception outputs 132), stopping(e.g., at a “Stop” sign identified in the perception outputs 132),passing other vehicles identified in the perception outputs 132,adjusting vehicle lane position to accommodate a bicyclist identified inthe perception outputs 132, slowing down in a school or constructionzone, merging (e.g., onto a highway), and parking. The planningdecisions generated by the planning subsystem 140 can be provided to acontrol system of the vehicle 102. The control system of the vehicle cancontrol some or all of the operations of the vehicle by implementing theplanning decisions generated by the planning system. For example, inresponse to receiving a planning decision to apply the brakes of thevehicle, the control system of the vehicle 102 may transmit anelectronic signal to a braking control unit of the vehicle. In responseto receiving the electronic signal, the braking control unit canmechanically apply the brakes of the vehicle.

In order for the planning subsystem 140 to generate planning decisionswhich cause the vehicle 102 to travel along a safe and comfortabletrajectory, the on-board system 100 must provide the planning subsystem140 with high quality perception outputs 132. Some existing approachesto classifying or detecting objects within point cloud data involvegenerating a representation of the point cloud and then processing therepresentation using a neural network. However, existing approaches usehard voxelization to generate a representation of the point cloud from asingle view of the scene, i.e., a birds-eye view, that assigns some ofthe points in the point cloud to voxels based on where the points liefrom the single view. However, such approaches can result in sub-optimalrepresentations that are not deterministic and that miss informationabout the scene that may be available from a different view, byconsidering all of the points in the point cloud, or both.

To alleviate some of these issues, the perception subsystem 130generates representations of point clouds using dynamic voxelization andin some cases fuses information from multiple views when generating aperception output.

FIG. 2 is a flow diagram of an example process 200 for processing pointcloud data. For convenience, the process 200 will be described as beingperformed by a system of one or more computers located in one or morelocations. For example, an on-board system, e.g., the on-board system100 of FIG. 1 , appropriately programmed in accordance with thisspecification, can perform the process 200.

The system obtains, i.e., receives or generates, point cloud datarepresenting a sensor measurement of a scene captured by one or moresensors (step 202). The point cloud data includes a featurerepresentation of each of a set of three-dimensional points, i.e., a setof points corresponding to reflections identified by one or more scansof the scene by the one or more sensors. Each three-dimensional pointgenerally has x, y, and z coordinates (or three different coordinates ina different coordinate system). As used in this specification, a featurerepresentation is an ordered collection of numeric values, e.g., amatrix or vector of floating point or quantized values.

For example, the system can receive raw sensor data that identifies theset of three-dimensional points and optionally includes featuresgenerated by the one or more sensors for the points, e.g., LiDARfeatures, and process the raw sensor data for each point using anembedding neural network to generate the feature representations foreach of the points.

The system generates, for each of one or more views of the scene, acorresponding dynamic voxel representation that assigns each of thethree-dimensional points in the point cloud data to a respective voxelof a variable number of voxels (step 204). In particular, unlikeconventional approaches, the dynamic voxel representation does not havea fixed number of voxels or a fixed number of points per voxel. Instead,the dynamic voxel representation has a variable number of voxels, i.e.,has different numbers of voxels for different sets of three-dimensionalpoints, and a variable number of points per voxel. Moreover, the dynamicvoxel representation defines a bi-directional mapping between voxels andpoints in the set of three-dimensional points, i.e., all of the pointsin the point cloud data are included in one of the voxels and no pointsare discarded.

To generate the dynamic voxel representation for a given view, thesystem assigns, based on the positions of the three-dimensional pointsin the point cloud data according to the view, each of thethree-dimensional points to a respective one of a set of voxels for thegiven view. As a particular example, the system can, for a given view,partition the scene into a fixed number of partitions according to thegiven view and can then assign each point to the voxel the point belongsto according to the given view. Any voxel that at least one point isassigned to is included in the dynamic voxel representation and eachpoint that is assigned to any voxel is also included in the dynamicvoxel representation, i.e., no points or voxels are discarded to satisfyfixed size requirements for numbers of voxels or numbers of points pervoxel as in conventional techniques. Because different views willpartition the scene differently and different views will assigndifferent points to different voxels, the dynamic voxel representationof the same point cloud data for two different views will generally bedifferent.

Generating the dynamic voxel representation is described in more detailbelow with reference to FIG. 3 .

The system then generates a network input from the dynamic voxelrepresentations corresponding to each of the one or more views (step206).

When there is a single view, e.g., a birds eye view or a perspectiveview, the system generates the network input by, processing, for eachvoxel in the dynamic voxel representation, the feature representationsof the three-dimensional points assigned to the voxel to generaterespective voxel feature representations of each of thethree-dimensional points assigned to the voxel. The system thengenerates the network input from, for each point, at least the voxelfeature representation for the point. For example, the network input caninclude, for each point, a combination, e.g., a concatenation, of thevoxel feature representation for the point and the featurerepresentation for the point.

When there are multiple views, e.g., a birds eye view and a perspectiveview, the system generates the network input by, for any given point,combining data for the point from each of the views. The system is ableto perform this combination for all of the points because, as describedabove, the dynamic voxel representation defines a bi-directional mappingbetween voxels and points in the set of three-dimensional points. Aswill be described in more detail below, this allows the system to, forall of the views, separately identify which voxel each point belongs toand to associate voxel-level features with each point from all of themultiple views.

In particular, for a given view, the system processes, for each voxel inthe dynamic voxel representation corresponding to the given view, thefeature representations of the three-dimensional points assigned to thevoxel to generate respective voxel feature representations of each ofthe three-dimensional points assigned to the voxel.

The system then generates a combined feature representation of thethree-dimensional point from at least the voxel feature representationsfor the three-dimensional point for each of the views. Thus, because ofthe use of dynamic voxelization, the system is effectively able tocombine information from multiple different views of the same scenecaptured by the same set of one or more sensors.

Generating the network input when there are multiple views is describedin more detail below with reference to FIG. 4 .

Once the network input is generated, the system processes using a neuralnetwork to generate a network output that characterizes the scene (step208).

The neural network can be any appropriate task neural network that isconfigured to generate a network output for the desired perception task.Examples of task neural networks are described in more detail below withreference to FIG. 4 .

FIG. 3 is an example of generating a dynamic voxel representation for aview of a scene.

In particular, FIG. 3 shows the generation of a dynamic voxelrepresentation for a view of the scene generated using dynamicvoxelization and a conventional voxel representation of the view of thescene generated using hard voxelization.

In general, the system assigns, based on positions of thethree-dimensional points in the point cloud data according to the view,each of the three-dimensional points to a respective one of the voxelsof the set of voxels.

As shown in FIG. 3 , a point cloud that includes thirteen points ispartitioned into four voxels V1, V2, V3, and V4, with six points beingassigned to V1, four points being assigned to V2, two points beingassigned to V3, and one point being assigned to V4. Each point is alsoassociated with features of dimension F. The voxels V1, V2, V3, and V4are determined by partitioning the scene into a fixed number ofpartitions according to the particular view of the scene and the pointsare assigned to the voxels by assigning each point to the voxel thepoint belongs to according to the given view.

That is, when the view is a birds-eye view, the voxels are generated bypartitioning the scene into a fixed number of partitions in a Cartesiancoordinate space and then assigning the three-dimensional points tovoxels based on positions of the three-dimensional points in theCartesian coordinate space.

When the view is a perspective view, the voxels are generated bypartitioning the scene into a fixed number of partitions in a sphericalcoordinate space and then assigning the three-dimensional points tovoxels based on positions of the three-dimensional points in thespherical coordinate space. A voxel in perspective view may also bereferred to as a three-dimensional frustum.

Because of the different coordinate systems used by the different views,the partitioning and the assignment will generally be different betweendifferent the different views, resulting in at least some of the pointsbeing grouped with different sets of other points for different views.

In hard voxelization, after voxel partitioning, the representation isgenerated by assigning the points to a buffer of fixed size K×T×F, whereK is the maximum number of voxels that can be represented, T is themaximum number of points per voxel, and F is the feature dimension ofeach point in the representation. In the example of FIG. 2 , K=3 andT=5. Because the dimensions K and T are fixed, the resultingrepresentation is always the same size. However, since a voxel may beassigned more points than its fixed point capacity T allows, in hardvoxelization a system sub-samples a fixed T number of points from eachvoxel. Similarly, if the point cloud produces more voxels than the fixedvoxel capacity K, the voxels are sub-sampled to yield K total voxels. Onthe other hand, when there are fewer points in a given voxel or fewertotal voxels than the fixed capacity T or V allows, the unused entriesin the representation are zero-padded. Thus, hard voxelization (HV) hasthree intrinsic limitations: (1) As points and voxels are dropped whenthey exceed the buffer capacity, HV forces the model that processes therepresentation to throw away information that may be useful fordetection; (2) This stochastic dropout of points and voxels may alsolead to non-deterministic voxel embeddings, and consequently unstable orjittery detection outcomes; (3) Voxels that are padded cost unnecessarycomputation, which hinders the run-time performance.

As can be seen from the “voxel occupancy” in FIG. 1 , hard voxelizationresults in a representation that has three voxels, V1, V3, and V4, withV1 having the maximum five entries, V3 having two entries, and V4 havingone entry. The remaining three entries for V3 and four entries in V4 arezero padded.

Thus, hard voxelization drops one point in V1 and misses V2 entirely,while still requiring 15F memory usage for the representation.

Dynamic voxelization can mitigate these limitations.

In particular, to generate the dynamic voxel representation for a givenview, the system assigns, based on the positions of thethree-dimensional points in the point cloud data according to the view,each of the three-dimensional points to a respective one of a set ofvoxels for the given view. As a particular example, the system can, fora given view, partition the scene into a fixed number of partitionsaccording to the given view and can then assign each point to the voxelthe point belongs to according to the given view.

Any voxel that at least one point is assigned to is included in thedynamic voxel representation and each point that is assigned to anyvoxel is also included in the dynamic voxel representation, i.e., nopoints or voxels are discarded to satisfy fixed size requirements fornumbers of voxels or numbers of points per voxel as in conventionaltechniques. Because different views will partition the scene differentlyand different views will assign different points to different voxels,the dynamic voxel representation of the same point cloud data for twodifferent views will generally be different.

In the example of FIG. 3 , the system generates a representation thatincludes six entries for V1, four entries for V2, two entries for V3,and one entry for V4, with no voxels or points being dropped and no zeropadding employed. This results in a memory usage of 13F. Thus, dynamicvoxelization captures all thirteen points with lower memory usage thanhard voxelization.

FIG. 4 shows an example process for applying multi-view fusion togenerate a network input from a raw input point cloud when there aremultiple views. In the example of FIG. 4 , there are two views: bird'seye view and perspective view.

In particular, the raw input point cloud includes raw sensor data foreach of multiple three-dimensional points and the system first processesthe raw sensor data using an embedding neural network (“shared FC”) togenerate point cloud data that includes a respective featurerepresentation for each of the three-dimensional points.

For example, the raw sensor data can include, for each point, the pointintensity as measured by the sensor that captured the sensor data andthe three-dimensional coordinates of the point. In this example, thesystem can process, for each point, the point intensity and therespective local coordinates of the point in the voxel to which thepoint was assigned in each of the multiple views using the embeddingneural network to generate the feature representation of the point.

The embedding neural network can be, for example, a fully-connected (FC)neural network. As a particular example, the embedding neural networkcan be composed of a linear layer, a batch normalization (BN) layer anda rectified linear unit (ReLU) layer.

The system then generates, for each of the views, a correspondingdynamic voxel representation that assigns, to each voxel of a set ofvoxels for the view, a variable number of three-dimensional points asdescribed above. As a result, the system also establishes, for eachview, a bi-directional mapping between voxels in the dynamic voxelrepresentation and the three-dimensional points in the point cloud data.The established point/voxel mappings are (F^(cart) _(V)(p_(i)), F^(cart)_(P)(v_(j))) and (F_(spheV)(p_(i)), F_(spheP)(v_(j))) for the birds-eyeview and the perspective view, respectively.

Within each view and for each voxel in the dynamic voxel representationcorresponding to the view, the system processes the featurerepresentations of the three-dimensional points assigned to the voxel togenerate respective voxel feature representations of each of thethree-dimensional points assigned to the voxel. In other words, thesystem generates a respective voxel feature representation for eachvoxel and then associates the voxel feature representation with eachpoint assigned to the voxel using the established mapping.

In particular, within each view and to generate the voxel featurerepresentations for the voxels corresponding to the view, the systemprocesses the feature representation for each point using an additionalneural network (“FC2”) to generate view-dependent features for thepoint. The additional neural network can also be a fully-connected (FC)neural network. As a particular example, the additional neural networkcan be composed of a linear layer, a batch normalization (BN) layer anda rectified linear unit (ReLU) layer.

Then, by referencing the point to voxel mapping, the system aggregatesvoxel-level information from the points within each voxel for the viewby applying pooling, e.g., max pooling (“maxpool”) or average pooling,to generate a voxel-wise feature map for the view. By performing thisaggregation, the system can effectively generate the voxel featurerepresentations even when different voxels have different numbers ofpoints.

In other words, within each view, the system separately generatesview-dependent features for each point and then aggregates theview-dependent features to generate voxel-level features for each voxelin the representation for the view.

As a particular example, the voxel-wise feature map can include arespective spatial location corresponding to each of the partitions ofthe scene in the corresponding view. For each partition that correspondsto a voxel, i.e., each partition to which at least one point wasassigned during voxelization, the features at the spatial locationcorresponding to the partition are the voxel-level features for thecorresponding voxel. For any partition that does not correspond to avoxel, i.e., any partition to which no points were assigned duringvoxelization, the features at the spatial location corresponding to thepartition are placeholder features, i.e., features set to zeroes oranother default value.

The system can then process the voxel-level feature map for the viewusing a convolutional neural network (“convolution tower”) to generatethe voxel feature representations (“context features”) for each of thevoxels in the view.

Finally, with each view and using the point-to-voxel mapping, the systemgathers voxel features per point (“gather voxel feature per point”). Inother words, for each point, the system associates the voxel featurerepresentation for the voxel to which the point belongs with the point.

By performing these operations for each view, the system generates, foreach point, respective voxel feature representations for each of theviews.

The system then generates a combined feature representation of thethree-dimensional point from at least the voxel feature representationsfor the three-dimensional point for each of the views. In particular,the system combines, e.g., concatenates, at least the voxel featurerepresentations for the three-dimensional point for each of the views togenerate the combined feature representation (“point-level semantics”).In the particular example of FIG. 4 , the system concatenates (“concat”)the voxel feature representations for the three-dimensional point foreach of the views and the feature representation generated by theembedding neural network to generate the combined feature representationfor a given point.

The system can then generate the network input by combining the combinedfeature representations of the three-dimensional points. For example,the system can scatter or otherwise generate a pseudo-image (an h×w×dfeature map) from the combined feature representations of thethree-dimensional points.

This network input, i.e., the pseudo-image, can then be processed by atask neural network, e.g., a conventional two-dimensional convolutionalneural network that has been configured to perform the desired task, togenerate a network output for the desired task. In some implementations,the system transforms the combined feature representation to a lowerfeature dimension, e.g., using a learned projection matrix, as part ofgenerating the network input to reduce computational cost.

For example, when the task is object detection, the task neural networkcan include a two-dimensional convolutional backbone neural network anda 3d object detection neural network head that is configured to processthe output of the backbone neural network to generate an objectdetection output that identifies locations of objects in the pointcloud, e.g., that identifies locations of bounding boxes in the imageand a likelihood that each bounding box includes an object.

An example of a convolutional neural network backbone and an objectdetection head is described in A. H. Lang, S. Vora, H. Caesar, L. Zhou,J. Yang, and O. Beijbom. Pointpillars: Fast encoders for objectdetection from point clouds. CVPR, 2019. However, the task neuralnetwork can generally have any appropriate architecture that maps anetwork input to an output for the desired task.

The system can train each of the neural network components describedwith reference to FIG. 4 jointly with the task neural network on groundtruth object detection outputs for point clouds in a set of trainingdata. For example, when the task is object detection, the loss functionused for the training of these neural networks can be an objectdetection loss that measures the quality of object detection outputsgenerated by the these neural networks relative to the ground truthobject detection outputs, e.g., smoothed losses for regressed values andcross entropy losses for classification outputs. Particular of examplesof loss functions that can be used for the training are described in Y.Yan, Y. Mao, and B. Li. Second: Sparsely embedded convolutionaldetection. Sensors, 18 (10):3337, 2018 and A. H. Lang, S. Vora, H.Caesar, L. Zhou, J. Yang, and O. Beijbom. Pointpillars: Fast encodersfor object detection from point clouds. CVPR, 2019.

This specification uses the term “configured” in connection with systemsand computer program components. For a system of one or more computersto be configured to perform particular operations or actions means thatthe system has installed on it software, firmware, hardware, or acombination of them that in operation cause the system to perform theoperations or actions. For one or more computer programs to beconfigured to perform particular operations or actions means that theone or more programs include instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the operations oractions.

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Embodiments of the subject matter described in thisspecification can be implemented as one or more computer programs, i.e.,one or more modules of computer program instructions encoded on atangible non transitory storage medium for execution by, or to controlthe operation of, data processing apparatus. The computer storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofone or more of them. Alternatively or in addition, the programinstructions can be encoded on an artificially generated propagatedsignal, e.g., a machine-generated electrical, optical, orelectromagnetic signal that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus.

The term “data processing apparatus” refers to data processing hardwareand encompasses all kinds of apparatus, devices, and machines forprocessing data, including by way of example a programmable processor, acomputer, or multiple processors or computers. The apparatus can alsobe, or further include, special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application specificintegrated circuit). The apparatus can optionally include, in additionto hardware, code that creates an execution environment for computerprograms, e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, or acombination of one or more of them.

A computer program, which may also be referred to or described as aprogram, software, a software application, an app, a module, a softwaremodule, a script, or code, can be written in any form of programminglanguage, including compiled or interpreted languages, or declarative orprocedural languages; and it can be deployed in any form, including as astand alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A program may, but neednot, correspond to a file in a file system. A program can be stored in aportion of a file that holds other programs or data, e.g., one or morescripts stored in a markup language document, in a single file dedicatedto the program in question, or in multiple coordinated files, e.g.,files that store one or more modules, sub programs, or portions of code.A computer program can be deployed to be executed on one computer or onmultiple computers that are located at one site or distributed acrossmultiple sites and interconnected by a data communication network.

In this specification, the term “database” is used broadly to refer toany collection of data: the data does not need to be structured in anyparticular way, or structured at all, and it can be stored on storagedevices in one or more locations. Thus, for example, the index databasecan include multiple collections of data, each of which may be organizedand accessed differently.

Similarly, in this specification the term “engine” is used broadly torefer to a software-based system, subsystem, or process that isprogrammed to perform one or more specific functions. Generally, anengine will be implemented as one or more software modules orcomponents, installed on one or more computers in one or more locations.In some cases, one or more computers will be dedicated to a particularengine; in other cases, multiple engines can be installed and running onthe same computer or computers.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby special purpose logic circuitry, e.g., an FPGA or an ASIC, or by acombination of special purpose logic circuitry and one or moreprogrammed computers.

Computers suitable for the execution of a computer program can be basedon general or special purpose microprocessors or both, or any other kindof central processing unit. Generally, a central processing unit willreceive instructions and data from a read only memory or a random accessmemory or both. The essential elements of a computer are a centralprocessing unit for performing or executing instructions and one or morememory devices for storing instructions and data. The central processingunit and the memory can be supplemented by, or incorporated in, specialpurpose logic circuitry. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio or video player, a game console, a Global PositioningSystem (GPS) receiver, or a portable storage device, e.g., a universalserial bus (USB) flash drive, to name just a few.

Computer readable media suitable for storing computer programinstructions and data include all forms of non volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto optical disks; andCD ROM and DVD-ROM disks.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's device in response to requests received from the web browser.Also, a computer can interact with a user by sending text messages orother forms of message to a personal device, e.g., a smartphone that isrunning a messaging application, and receiving responsive messages fromthe user in return.

Data processing apparatus for implementing machine learning models canalso include, for example, special-purpose hardware accelerator unitsfor processing common and compute-intensive parts of machine learningtraining or production, i.e., inference, workloads.

Machine learning models can be implemented and deployed using a machinelearning framework, e.g., a TensorFlow framework, a Microsoft CognitiveToolkit framework, an Apache Singa framework, or an Apache MXNetframework.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface, a web browser, or anapp through which a user can interact with an implementation of thesubject matter described in this specification, or any combination ofone or more such back end, middleware, or front end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (LAN) and a widearea network (WAN), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data, e.g., an HTML page, to a userdevice, e.g., for purposes of displaying data to and receiving userinput from a user interacting with the device, which acts as a client.Data generated at the user device, e.g., a result of the userinteraction, can be received at the server from the device.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or on the scope of what may be claimed, but rather asdescriptions of features that may be specific to particular embodimentsof particular inventions. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially be claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings and recited inthe claims in a particular order, this should not be understood asrequiring that such operations be performed in the particular ordershown or in sequential order, or that all illustrated operations beperformed, to achieve desirable results. In certain circumstances,multitasking and parallel processing may be advantageous. Moreover, theseparation of various system modules and components in the embodimentsdescribed above should not be understood as requiring such separation inall embodiments, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. As one example, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In some cases, multitasking and parallel processing may beadvantageous.

What is claimed is:
 1. A method comprising: obtaining point cloud datarepresenting a sensor measurement of a scene captured by a sensor, thepoint cloud data comprising a respective feature representation for eachof a plurality of three-dimensional points in the scene; generating, foreach of one or more views of the scene, a corresponding dynamic voxelrepresentation that assigns, to each voxel of a set of voxels for theview, a variable number of three-dimensional points, wherein eachthree-dimensional point in the point cloud data is assigned to arespective one of the voxels of the set of voxels in the correspondingdynamic voxel representation, and wherein the generating comprises:assigning, based on positions of the three-dimensional points in thepoint cloud data according to the view, each of the three-dimensionalpoints to a respective one of the voxels of the set of voxels; andprocessing the dynamic voxel representations corresponding to each ofthe one or more views to generate an output that characterizes thescene.
 2. The method of claim 1, wherein obtaining the point cloud datacomprises: obtaining raw sensor data for each of the three-dimensionalpoints; and processing the raw sensor data using an embedding neuralnetwork to generate the point cloud data.
 3. The method of claim 1,wherein the output is an object detection output that identifies objectsthat are located in the scene.
 4. The method of claim 1, wherein thesensor is a LiDAR sensor.
 5. The method of claim 1, wherein a first viewof the one or more views is a birds-eye view, and wherein assigning eachof the three-dimensional points to a respective one of the of voxels inthe dynamic voxel representation corresponding to the birds-eye viewcomprises assigning the three-dimensional points to voxels based onpositions of the three-dimensional points in a Cartesian coordinatespace.
 6. The method of claim 1, wherein a second view of the one ormore views is a perspective view, and wherein assigning each of thethree-dimensional points to a respective one of the voxels in thedynamic voxel representation corresponding to the perspective viewcomprises assigning the three-dimensional points to voxels based onpositions of the three-dimensional points in a spherical coordinatespace.
 7. The method of claim 1, wherein generating the network inputcomprises, for each of the one or more views: for each voxel in thedynamic voxel representation corresponding to the view, processing thefeature representations of the three-dimensional points assigned to thevoxel to generate respective voxel feature representations of each ofthe three-dimensional points assigned to the voxel.
 8. The method ofclaim 7, wherein the one or more views comprise a plurality of views andwherein generating the network input comprises, for each of thethree-dimensional points in the point cloud data: generating a combinedfeature representation of the three-dimensional point from at least thevoxel feature representations for the three-dimensional point for eachof the views; and generating the network input by combining the combinedfeature representations of the three-dimensional points.
 9. The methodof claim 8, wherein generating the combined feature representation ofthe three-dimensional point comprises concatenating the voxel featurerepresentations for the three-dimensional point for each of the viewsand the feature representation for the three-dimensional point in thepoint cloud data.
 10. The method of claim 1, wherein, for each of theone or more views, the dynamic voxel representation corresponding to theview defines a bi-directional mapping between voxels in the dynamicvoxel representation and the three-dimensional points in the point clouddata.
 11. A system comprising one or more computers and one or morestorage devices storing instructions that when executed by the one ormore computers cause the one or more computers to perform operationscomprising: obtaining point cloud data representing a sensor measurementof a scene captured by a sensor, the point cloud data comprising arespective feature representation for each of a plurality ofthree-dimensional points in the scene; generating, for each of one ormore views of the scene, a corresponding dynamic voxel representationthat assigns, to each voxel of a set of voxels for the view, a variablenumber of three-dimensional points, wherein each three-dimensional pointin the point cloud data is assigned to a respective one of the voxels ofthe set of voxels in the corresponding dynamic voxel representation, andwherein the generating comprises: assigning, based on positions of thethree-dimensional points in the point cloud data according to the view,each of the three-dimensional points to a respective one of the voxelsof the set of voxels; and processing the dynamic voxel representationscorresponding to each of the one or more views to generate an outputthat characterizes the scene.
 12. The system of claim 11, whereinobtaining the point cloud data comprises: obtaining raw sensor data foreach of the three-dimensional points; and processing the raw sensor datausing an embedding neural network to generate the point cloud data. 13.The system of claim 11, wherein the output is an object detection outputthat identifies objects that are located in the scene.
 14. The system ofclaim 11, wherein a first view of the one or more views is a birds-eyeview, and wherein assigning each of the three-dimensional points to arespective one of the of voxels in the dynamic voxel representationcorresponding to the birds-eye view comprises assigning thethree-dimensional points to voxels based on positions of thethree-dimensional points in a Cartesian coordinate space.
 15. The systemof claim 11, wherein a second view of the one or more views is aperspective view, and wherein assigning each of the three-dimensionalpoints to a respective one of the voxels in the dynamic voxelrepresentation corresponding to the perspective view comprises assigningthe three-dimensional points to voxels based on positions of thethree-dimensional points in a spherical coordinate space.
 16. The systemof claim 11, wherein generating the network input comprises, for each ofthe one or more views: for each voxel in the dynamic voxelrepresentation corresponding to the view, processing the featurerepresentations of the three-dimensional points assigned to the voxel togenerate respective voxel feature representations of each of thethree-dimensional points assigned to the voxel.
 17. The system of claim16, wherein the one or more views comprise a plurality of views andwherein generating the network input comprises, for each of thethree-dimensional points in the point cloud data: generating a combinedfeature representation of the three-dimensional point from at least thevoxel feature representations for the three-dimensional point for eachof the views; and generating the network input by combining the combinedfeature representations of the three-dimensional points.
 18. The systemof claim 17, wherein generating the combined feature representation ofthe three-dimensional point comprises concatenating the voxel featurerepresentations for the three-dimensional point for each of the viewsand the feature representation for the three-dimensional point in thepoint cloud data.
 19. The system of claim 11, wherein, for each of theone or more views, the dynamic voxel representation corresponding to theview defines a bi-directional mapping between voxels in the dynamicvoxel representation and the three-dimensional points in the point clouddata.
 20. One or more non-transitory computer-readable media storinginstructions that when executed by one or more computers cause the oneor more computers to perform operations comprising: obtaining pointcloud data representing a sensor measurement of a scene captured by asensor, the point cloud data comprising a respective featurerepresentation for each of a plurality of three-dimensional points inthe scene; generating, for each of one or more views of the scene, acorresponding dynamic voxel representation that assigns, to each voxelof a set of voxels for the view, a variable number of three-dimensionalpoints, wherein each three-dimensional point in the point cloud data isassigned to a respective one of the voxels of the set of voxels in thecorresponding dynamic voxel representation, and wherein the generatingcomprises: assigning, based on positions of the three-dimensional pointsin the point cloud data according to the view, each of thethree-dimensional points to a respective one of the voxels of the set ofvoxels; and processing the dynamic voxel representations correspondingto each of the one or more views to generate a output that characterizesthe scene.